package com.gxkj.spark.stream;

import java.util.Arrays;
import java.util.List;
import java.util.regex.Pattern;

import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.spark.streaming.Duration;
import scala.Tuple2;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.*;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.Optional;
import org.apache.spark.api.java.StorageLevels;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.State;
import org.apache.spark.streaming.StateSpec;
import org.apache.spark.streaming.api.java.*;

/**
 * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every
 * second starting with initial value of word count.
 * Usage: JavaStatefulNetworkWordCount <hostname> <port>
 * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
 * data.
 * <p>
 * To run this on your local machine, you need to first run a Netcat server
 * `$ nc -lk 9999`
 *  windows环境输入命令：nc -l -p 9999
 * and then run the example
 * `$ bin/run-example
 * org.apache.spark.examples.streaming.JavaStatefulNetworkWordCount localhost 9999`
 */
public class JavaStatefulNetworkWordCount {
    private static final Pattern SPACE = Pattern.compile(" ");

    /**
     * vm -Dspark.master=local
     * args[0] 是host;
     * args[1] 是port;
     * @param args
     * @throws Exception
     */
    public static void main(String[] args) throws Exception {
        args = new String[]{"localhost","9999"};
        if (args.length < 2) {
            System.err.println("Usage: JavaStatefulNetworkWordCount <hostname> <port>");
            System.exit(1);
        }
//        实际上本地通过设置log4j来设置开发log级别
//        Logger.getRootLogger().setLevel(Level.WARN);
//        Logger.getLogger(JavaStatefulNetworkWordCount.class).setLevel(Level.WARN);

//        Logger.getRootLogger.setLevel(Level.WARN);
       // StreamingExamples.setStreamingLogLevels();

//       创建context，1秒钟处理一下批次
        // Create the context with a 1 second batch size
        SparkConf sparkConf = new SparkConf().setAppName("JavaStatefulNetworkWordCount");
        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(5));
        ssc.checkpoint(".");

        // Initial state RDD input to mapWithState
        @SuppressWarnings("unchecked")
        List<Tuple2<String, Integer>> tuples =
                Arrays.asList(new Tuple2<>("hello", 1), new Tuple2<>("world", 1));
        JavaPairRDD<String, Integer> initialRDD = ssc.sparkContext().parallelizePairs(tuples);

        // Create a DStream that will connect to hostname:port, like localhost:9999
        JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
                args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER_2);

        JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());

        JavaPairDStream<String, Integer> wordsDstream = words.mapToPair(s -> new Tuple2<>(s, 1));

        // Update the cumulative count function
        Function3<String, Optional<Integer>, State<Integer>, Tuple2<String, Integer>> mappingFunc =
                (word, one, state) -> {
                    int sum = one.orElse(0) + (state.exists() ? state.get() : 0);
                    Tuple2<String, Integer> output = new Tuple2<>(word, sum);
                    state.update(sum);
                    return output;
                };

        // DStream made of get cumulative counts that get updated in every batch
        JavaMapWithStateDStream<String, Integer, Integer, Tuple2<String, Integer>> stateDstream =
                wordsDstream.mapWithState(StateSpec.function(mappingFunc).initialState(initialRDD));

        stateDstream.print();
        ssc.start();
        ssc.awaitTermination();
    }
}